Large Scale
Learning from Data Streams

18 September 2017

IoT Stream for Data Driven Predictive Maintenance


14 September 2020

The Workshop

Workshop + Tutorial
18 September 2017
09:00 am

The volume of data is rapidly increasing due to the development of the technology of information and communication. This data comes mostly in the form of streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. In addition, these models must take into account many constraints: (pseudo) real-time processing, high-velocity, and dynamic multi-form change such as concept drift and novelty. This workshop welcomes novel research about learning from data streams in evolving environments. It will provide the researchers and participants with a forum for exchanging ideas, presenting recent advances and discussing challenges related to data streams processing. It solicits original work, already completed or in progress. Position papers are also considered. This workshop is combined with a tutorial treating the same topic and will be presented in the same day.


The Workshop

ECML/PKDD 2020 Workshop on
IoT Stream for Data Driven Predictive Maintenance

Maintenance is a critical issue in the industrial context for the prevention of high costs or injuries. Various industries are moving more and more toward digitalization and collecting “big data” to enable or improve the accuracy of their prediction. At the same time, the emerging technologies of Industry 4.0 empowered data production and exchange which lead to new concepts and methodologies exploitation of large datasets for maintenance. The intensive research effort in data-driven Predictive Maintenance (PdM) has been producing encouraging outcomes. Therefore, the main objective of this workshop is to raise awareness of research trends and promote interdisciplinary discussion in this field.

Data-driven predictive maintenance deals with big streaming data that include concept drift due to both changing external conditions, but also normal wear of the equipment. It requires combining multiple data sources, and the resulting datasets are often highly imbalanced. The knowledge about the systems is detailed but in many scenarios, there is a large diversity in both model configurations, as well as their usage, additionally complicated by low data quality and high uncertainty in the labels. In particular, many recent advancements in supervised and unsupervised machine learning, representation learning, anomaly detection, visual analytics and similar areas can be showcased in this domain. Therefore the overlap in research between machine learning and predictive maintenance continues to increase in recent years.

Maintenance is a crucial topic for industrial machines, medical equipment, energy systems, passengers transport vehicles and home appliances among others. Cost reduction, machine reliability, operation, safety and time reduction have been the main concerns of companies and organizations. Meanwhile, Industry 4.0 brought new opportunities of meaningful data collection and storage. Promising data-driven methodologies shrive for predictive maintenance becoming a strong alternative.

Motivation and focus

This workshop will be centred on questions such as when to perform a maintenance action? How to estimate components current and future status? How accurate are the existing methods? Which data should be used? What decisions tools should be developed for prognostic. How can Data Mining and Machine Learning (Artificial Intelligence in general) contribute to answering these questions?

Therefore, this event is an opportunity to bridge researchers and engineers to discuss the emerging topics and the key trends. Previous edition of the workshop at ECML 2019 has been very popular, and we are planning to continue this success in 2020.

Aim and scope

Topics of interest for the workshop include, but are not limited to:

  • Fault Detection and Diagnosis (FDD)

  • Fault Isolation and Identification

  • Estimation of Remaining Useful Life of Components, Machines, ….

  • Forecasting of Product and Process Quality

  • Early Failure and Anomaly Detection and Analysis

  • Automatic Process Optimization

  • Predictive and prescriptive maintenance

  • Self-healing and Self-correction

  • Incremental, evolving (data-driven and hybrid) models for FDD and anomaly detection

  • Self-adaptive time-series based models for prognostics and forecasting

  • Adaptive signal processing techniques for FDD and forecasting

  • Concept Drift issues in dynamic predictive maintenance systems

  • Active learning and Design of Experiment (DoE) in dynamic predictive maintenance

  • Systems Fault tolerant control

  • Industrial process monitoring and modelling

  • Maintenance scheduling and on-demand maintenance planning

  • Visual analytics and interactive Machine Learning

  • Decision-making assistance and resource optimization

  • Planning under uncertainty

  • Analysis of usage patterns

  • Explainable AI for predictive maintenance

Real world applications such as:

  • Manufacturing systems

  • Production Processes and Factories of the Future (FoF)

  • Wind turbines (offshore/onshore/floating)

  • Smart management of energy demand/response

  • Energy and power systems and networks

  • Transport systems

  • Power generation and distribution systems

  • Intrusion detection and cyber security

  • Internet of Things,

  • Next Generation Aerospace Applications, etc.

  • Big Data challenges in energy transition and digital transition

  • Solar plant monitoring and management

  • Active demand response

  • Distributed renewable energy management and integration into smart grids

  • Smart cities

Submission and Review process

Regular and short papers presenting work completed or in progress are invited. Regular papers should not exceed 12 pages, while short papers are maximum 6 pages. Papers must be written in English and are to be submitted in PDF format online via the Easychair submission interface:


Each submission will be evaluated on the basis of relevance, significance of contribution, quality of presentation and technical quality by at least two members of the program committee. All accepted papers will be included in the workshop proceedings and will be publically available on the conference web site. At least one author of each accepted paper is required to attend the workshop to present.

Important dates


Paper submission deadline:       9th of June 2020​
Paper acceptance notification:   20th of July 2020
Paper camera-ready deadline:     27th of July 2020


Program Committee members (to be confirmed)


  • Rita Ribeiro, University of Porto, Porto, Portugal

  • Carlos Ferreira, LIAAD INESC Porto LA, ISEP, Portugal

  • Edwin Lughofer, Johannes Kepler University of Linz, Austria

  • Sylvie Charbonnier, Université Joseph Fourier-Grenoble, France

  • David Camacho Fernandez, Universidad Politecnica de Madrid, Spain

  • Bruno Sielly Jales Costa, IFRN, Natal, Brazil

  • Fernando Gomide, University of Campinas, Brazil

  • José A. Iglesias, Universidad Carlos III de Madrid, Spain

  • Anthony Fleury, Mines-Douai, Institut Mines-Télécom, France

  • Teng Teck Hou, Nanyang Technological University, Singapore

  • Plamen Angelov, Lancaster University, UK

  • Igor Skrjanc, University of Ljubljana, Slovenia

  • Indre Zliobaite, University of Helsinki, Finland

  • Elaine Faria, Univ. Uberlandia, Brazil

  • Mykola Pechenizkiy, TU Eindonvhen, Netherlands

  • Raquel Sebastião, Univ. Aveiro, Portugal

  • Bruno Veloso, University of Porto, Porto, Portugal

  • Anders Holst, RISE SICS, Sweden

  • Erik Frisk, Linköping University, Sweden

  • Enrique Alba, University of Málaga, Spain

  • Thorsteinn Rögnvaldsson, Halmstad University, Sweden

  • Andreas Theissler, University of Applied Sciences Aalen, Germany

  • Vivek Agarwal, Idaho National Laboratory, Idaho

  • Manuel Roveri, Politecnico di Milano, Italy

  • Yang Hu, Politecnico di Milano, italy

Workshop Organizers

  • Grzegorz J. Nalepa, Jagiellonian University, Krakow, Poland



The Tutorial

Tutorial: IoT Data Stream Mining in Practice

The challenge of deriving insights from the Internet of Things (IoT) has
been recognized as one of the most exciting and key opportunities for both academia
and industry. The advent of IoT applications is here: industry 4.0, connected indus-
try, industry automation, smart cities, smart grids, energy efficiency, etc. All this IoT
applications require advanced analysis of big data streams from sensors and small
devices, while addressing security and privacy concerns. This tutorial is a gentle
introduction to mining IoT big data streams. The first part introduces data stream
learners for several learning tasks including distributed algorithms. The second and third part
present some applications for predictive maintenance, prediction for renewable ener-
gies, and social network analysis for telecommunications data streams.  The last part presents how to use Apache Spark Streaming for applying scalable machine learning on Big Data streams.


1.IoT Fundamentals and IoT Stream Mining Algorithms
– Predictive Learning
– Descriptive Learning
– Frequent Pattern mining
2. Case Study: Predictive Maintenance
– Problem Definition
– Change, Anomaly and Novelty Detection
– Failure Prediction and Detection
3. Case Study: Social Network Analysis

– Challenges in mining networked data,

– Online sampling

– Evolving centralities and communities

– Tracking the dynamics of evolving communities

4. Big Data Stream Mining using Spark Streaming
– Fundamental concepts
– Examples

  • Joao Gama

  • Rita Ribeiro

  • Moamar Sayed-Mouchaweh

  • Heitor Murilo Gomes

  • Latifur Khan

  • Albert Bifet


The Tutorial

Tutorial: IoT Stream for Data Driven Predictive Maintenance

Predictive Maintenance builds on monitoring the condition of specific equipment, observing its history as well as forecasting its future usage and wear trends – in order to identify the optimum time for maintenance. In most industries today, unexpected downtimes and failures are very costly and accurate predictions imminent issues become crucially important. PM addresses not only early detection of machine failures but also degraded performance and trends in product quality as well as appropriate actions to be taken. Recent developments in the field are based on different Machine Learning and Artificial Intelligence methods for fully- and semi-automated data-driven pattern recognition and knowledge creation. Enabled by IoT streams, self-adaptive models are necessary to handle complex system dynamics and non-stationary environments.

This tutorial aims to present current trends and promising research directions within the field of Machine Learning for Predictive Maintenance. We will present state-of-the-art methodologies to tackle these issues, as well as their application in real-world settings. This year the focus will be on Transfer Learning and Representation Learning, as the two of the most actively researched techniques in the area. We will also present a discussion about future challenges and open issues. A case study related to Predictive Maintenance challenges in the automotive industry will be presented by invited speaker(s) from Volvo Trucks.


The predictive maintenance paradigm significantly improves upon preventive maintenance by building on methods for “understanding” individual health status of each individual piece of equipment. This allows taking into account, among others, that usage profiles can be very different, due to global aspects (e.g. climate, education levels, and maintenance culture) or the particulars of the assignment (e.g. digging rocks or gravel, driving on the highway or in the city, etc.). Many market analysis reports indicate that the predictive maintenance market will grow very rapidly in the coming years. IoT, digitization and “big data,” faster computers with cheap memory, and especially machine learning algorithms that learn from very large data sets have demonstrated the possibility of making such specific predictions with very high accuracy.

Predictive maintenance is quickly moving away from diagnostic functions manually designed and tested by teams by domain expert engineers and embraces new developments in Machine Learning and Artificial Intelligence. These problems fit perfectly aware systems research, i.e., the research on systems that construct knowledge (semi-)automatically from observing real-world data. In most domains “normal” operation of a system is difficult to characterize precisely before deployment, and what is “normal” will vary throughout the lifetime of the equipment. Similarly, possible faults are often unknown or hard to describe precisely. Finally, due to the cost and design reasons, it is never possible to measure everything of interest; the condition of the equipment needs to be evaluated based on data that is available because it is convenient to capture – not necessarily the ideal data.

All these challenges combine to make Predictive Maintenance a very interesting testbed for modern Machine Learning algorithms. This tutorial will be interesting both for practitioners working in the area of Predictive Maintenance (who will get a chance to better understand new developments in Machine Learning as potential solutions) as well as for researchers developing new Machine Learning algorithms (presenting to them somewhat unique challenges present in the Predictive Maintenance setting and identify new promising directions of research and improvement).

Tutorial outline

  1. Introduction

    1. Definition and general principles

    2. Challenges and motivation

    3. Overview of real-world applications requiring predictive maintenance

  2. Representation learning for predictive maintenance

    1. Manual feature engineering

    2. State-of-the-art data-driven approaches

    3. Evaluation metrics

  3. Domain Adaptation for predictive maintenance

    1. Motivation and different scenarios

    2. Challenges and state-of-the-art techniques

    3. Application examples

  4. Case study: predictive maintenance in the automotive industry

  5. Conclusion and discussion

Tutorial Organisers:



Keynote Talk

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With the collaboration of  the project FailStopper (DSAIPA/DS/0086/2018), funded by FCT.

IoT Stream for Data Driven Predictive Maintenance




14 Setember 2020

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